日内现货波动率的非参数估计:分离瞬时趋势和季节性

Thibault Vatter, Hau‐Tieng Wu, V. Chavez-Demoulin, Bin Yu
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引用次数: 6

摘要

我们为高频金融数据的趋势和周期模式建模提供了一个新的框架。为了寻求对不断变化的市场条件的适应性,我们将傅里叶弹性形式扩展为更丰富的函数类:我们的平滑趋势和季节性都是非参数时变的,并且是实时演变的。我们提供了相关的估计器,并使用模拟来证明它们在存在跳跃和异方差和重尾噪声的情况下表现良好。一项对2010年至2013年汇率回报抽样的研究表明,未能考虑季节性的动态特性可能导致对当日现货波动的错误估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Non-Parametric Estimation of Intraday Spot Volatility: Disentangling Instantaneous Trend and Seasonality
We provide a new framework for modeling trends and periodic patterns in high-frequency financial data. Seeking adaptivity to ever-changing market conditions, we enlarge the Fourier flexible form into a richer functional class: both our smooth trend and the seasonality are non-parametrically time-varying and evolve in real time. We provide the associated estimators and use simulations to show that they behave adequately in the presence of jumps and heteroskedastic and heavy-tailed noise. A study of exchange rate returns sampled from 2010 to 2013 suggests that failing to factor in the seasonality’s dynamic properties may lead to misestimation of the intraday spot volatility.
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